Biosignal and Biomedical Image Processing phần 5 ppt

152 Chapter 6 of the instantaneous autocorrelation function, but only along the τ (i.e., lag ) dimension. The result is a function of both frequency and time. When the one- dimensional power spectrum was computed using the autocorrelation function, it was common to filter the autocorrelation function before taking the Fourier transform to improve features of the resulting power spectrum. While no such filtering is done in constructing the Wigner-Ville distribution, all of the other approaches apply a filter (in this case a two-dimensional filter) to the instanta- neous autocorrelation function before taking the Fourier transform. In fact, the primary difference between many of the distributions in Cohen’s class is simply the type of filter that is used. The formal equation for determining a time–frequency distribution from Cohen’s class of distributions is rather formidable, but can be simplified in practice. Specifically, the general equation is: ρ(t,f) = ∫∫∫g(v,τ)e j2πv(u −τ) x(u + 1 2 τ)x*(u − 1 2 τ)e −j2πfr dv du dτ (8) where g(v,τ) provides the two-dimensional filtering of the instantaneous auto- correlation and is also know as a kernel. It is this filter-like function that differ- entiates between the various distributions in Cohen’s class. Note that the rest of the integrand is the Fourier transform of the instantaneous autocorrelation function. There are several ways to simplify Eq. (8) for a specific kernel. For the Wigner-Ville distribution, there is no filtering, and the kernel is simply 1 (i.e., g(v,τ) = 1) and the general equation of Eq. (8), after integration by dv, reduces to Eq. (9), presented in both continuous and discrete form. W(t,f) = ∫ ∞ −∞ e −j2 πf τ x(t − τ 2 )x(t − τ 2 )dτ (9a) W(n,m) = 2 ∑ ∞ k=−∞ e −2πnm/N x(n + k)x*(n − k)(9b) W(n,m) = ∑ ∞ m=−∞ e −2πnm/N R x (n,k) = FFT k [R x (n,k)] (9c) Note that t = nT s ,andf = m/(NT s ) The Wigner-Ville has several advantages over the STFT, but also has a number of shortcomings. It greatest strength is that produces “a remarkably good picture of the time-frequency structure” (Cohen, 1992). It also has favor- able marginals and conditional moments. The marginals relate the summation over time or frequency to the signal energy at that time or frequency. For exam- ple, if we sum the Wigner-Ville distribution over frequency at a fixed time, we get a value equal to the energy at that point in time. Alternatively, if we fix TLFeBOOK Time–Frequency Analysis 153 frequency and sum over time, the value is equal to the energy at that frequency. The conditional moment of the Wigner-Ville distribution also has significance: f inst = 1 p(t) ∫ ∞ −∞ fρ(f,t)df (10) where p(t) is the marginal in time. This conditional moment is equal to the so-called instantaneous fre- quency. The instantaneous frequency is usually interpreted as the average of the frequencies at a given point in time. In other words, treating the Wigner-Ville distribution as an actual probability density (it is not) and calculating the mean of frequency provides a term that is logically interpreted as the mean of the frequencies present at any given time. The Wigner-Ville distribution has a number of other properties that may be of value in certain applications. It is possible to recover the original signal, except for a constant, from the distribution, and the transformation is invariant to shifts in time and frequency. For example, shifting the signal in time by a delay of T seconds would produce the same distribution except shifted by T on the time axis. The same could be said of a frequency shift (although biological processes that produce shifts in frequency are not as common as those that produce time shifts). These characteristics are also true of the STFT and some of the other distributions described below. A property of the Wigner-Ville distri- bution not shared by the STFT is finite support in time and frequency. Finite support in time means that the distribution is zero before the signal starts and after it ends, while finite support in frequency means the distribution does not contain frequencies beyond the range of the input signal. The Wigner-Ville does contain nonexistent energies due to the cross products as mentioned above and observed in Figure 6.1, but these are contain ed within the time and frequen cy boundaries of the original signal. Due to these cross products, the Wigner-Ville distribution is not necessarily zero whenever the signal is zero, a property Cohen called strong finite support. Obviously, since the STFT does not have finite sup- port it does not have strong finite supp ort. A few of the other distributions do have strong finite support. Examples of the desira ble attributes of the Wi gner-Ville will be explored in the MATLAB Implemen tatio n section, and in the problems. The Wigner-Ville distribution has a number of shortcomings. Most serious of these is the production of cross products: the demonstration of energies at time–frequency values where they do not exist. These phantom energies have been the prime motivator for the development of other distributions that apply various filters to the instantaneous autocorrelation function to mitigate the dam- age done by the cross products. In addition, the Wigner-Ville distribution can have negative regions that have no meaning. The Wigner-Ville distribution also has poor noise properties. Essentially the noise is distributed across all time and TLFeBOOK 154 Chapter 6 frequency including cross products of the noise, although in some cases, the cross products and noise influences can be reduced by using a window. In such cases, the desired window function is applied to the lag dimension of the instantaneous autocorrelation function (Eq. (7)) similar to the way it was applied to the time function in Chapter 3. As in Fourier transform analysis, windowing will reduce frequency resolution, and, in practice, a compromise is sought be- tween a reduction of cross products and loss of frequency resolution. Noise properties and the other weaknesses of the Wigner-Ville distribution along with the influences of windowing are explored in the implementation and problem sections. The Choi-Williams and Other Distributions The existence of cross products in the Wigner-Ville transformation has motived the development of other distributions. These other distributions are also defined by Eq. (8); however, now the kernel, g(v,τ), is no longer 1. The general equation (Eq. (8)) can be simplified two different ways: for any given kernel, the integra- tion with respect to the variable v can be performed in advance since the rest of the transform (i.e., the signal portion) is not a function of v; or use can be made of an intermediate function, called the ambiguity function. In the first approach, the kernel is multiplied by the exponential in Eq. (9) to give a new function, G(u,τ): G(u,τ) = ∫ ∞ −∞ g(v,τ)e jπvu dv (11) where the new function, G(u,τ) is referred to as the determining function (Boashash and Reilly, 1992). Then Eq. (9) reduces to: ρ(t,f) =∫∫G(u − t,τ)x(u + 1 2 τ)x*(u − 1 2 τ)e −2πf τ dudτ (12) Note that the second set of terms under the double integral is just the instantaneous autocorrelation function given in Eq. (7). In terms of the determin- ing function and the instantaneous autocorrelation function, the discrete form of Eq. (12) becomes: ρ(t,f) = ∑ M τ=0 R x (t,τ)G(t,τ)e −j2 πf τ (13) where t = u/f s . This is the approach that is used in the section on MATLAB implementation below. Alternatively, one can define a new function as the in- verse Fourier transform of the instantaneous autocorrelation function: A x (θ,τ) ∆ = IFT t [x(t +τ/2)x*(t −τ/2)] = IFT t [R x (t,τ)] (14) TLFeBOOK Time–Frequency Analysis 155 where the new function, A x (θ,τ), is termed the ambiguity function. In this case, the convolution operation in Eq. (13) becomes multiplication, and the desired distribution is just the double Fourier transform of the product of the ambiguity function times the instantaneous autocorrelation function: ρ(t,f) = FFT t {FFT f [A x (θ,τ)R x (t,τ)]} (15) One popular distribution is the Choi-Williams, which is also referred to as an exponential distribution (ED) since it has an exponential-type kernel. Specifi- cally, the kernel and determining function of the Choi-Williams distribution are: g(v,τ) = e −v 2 τ 2 /σ (16) After integrating the equation above as in Eq. (11), G(t,τ) becomes: G(t,τ) = √ σ/π 2τ e −σt 2 /4τ 2 (17) The Choi-Williams distribution can also be used in a modified form that incorporates a window function and in this form is considered one of a class of reduced interference distributions (RID) (Williams, 1992). In addition to having reduced cross products, the Choi-Williams distribution also has better noise characteristics than the Wigner-Ville. These two distributions will be compared with other popular distributions in the section on implementation. Analytic Signal All of the transformations in Cohen’s class of distributions produce better results when applied to a modified version of the waveform termed the Analytic signal , a complex version of the real signal. While the real signal can be used, the analytic signal has several advantages. The most important advantage is due to the fact that the analytic signal does not contain negative frequencies, so its use will reduce the number of cross products. If the real signal is used, then both the positive and negative spectral terms produce cross products. Another benefit is that if the analytic signal is used the sampling rate can be reduced. This is because the instantaneous autocorrelation function is calculated using evenly spaced values, so it is, in fact, undersampled by a factor of 2 (compare the discrete and continuous versions of Eq. (9)). Thus, if the analytic function is not used, the data must be sampled at twice the normal minimum; i.e., twice the Nyquist frequency or four times f MAX .* Finally, if the instantaneous frequency *If the waveform has already been sampled, the number of data points should be doubled with intervening points added using interpolation. TLFeBOOK 156 Chapter 6 is desired, it can be determined from the first moment (i.e., mean) of the distri- bution only if the analytic signal is used. Several approaches can be used to construct the analytic signal. Essen- tially one takes the real signal and adds an imaginary component. One method for establishing the imaginary component is to argue that the negative frequen- cies that are generated from the Fourier transform are not physical and, hence, should be eliminated. (Negative frequencies are equivalent to the redundant fre- quencies above f s /2. Following this logic, the Fourier transform of the real signal is taken, the negative frequencies are set to zero, or equivalently, the redundant frequencies above f s /2, and the (now complex) signal is reconstructed using the inverse Fourier transform. This approach also multiplies the positive frequen- cies, those below f s /2, by 2 to keep the overall energy the same. This results in a new signal that has a real part identical to the real signal and an imaginary part that is the Hilbert Transform of the real signal (Cohen, 1989). This is the approach used by the MATLAB routine hilbert and the routine hilber on the disk, and the approach used in the examples below. Another method is to perform the Hilbert transform directly using the Hilbert transform filter to produce the complex component: z(n) = x(n) + j H[x(n)] (18) where H denotes the Hilbert transform, which can be implemented as an FIR filter (Chapter 4) with coefficients of: h(n) = ͭ 2 sin 2 (πn/2) πn for n ≠ 0 0forn = 0 (19) Although the Hilbert transform filter should have an infinite impulse re- sponse length (i.e., an infinite number of coefficients), in practice an FIR filter length of approximately 79 samples has been shown to provide an adequate approximation (Bobashash and Black, 1987). MATLAB IMPLEMENTATION The Short-Term Fourier Transform The implementation of the time–frequency algorithms describ ed above is straight- forward and is illustrated in the examples below. The spectrogram can be gener- ated using the standard fft function described in Chapter 3, or using a special function of the Signal Processing Toolbox, specgram . The arguments for spec- gram (given on the next page) are similar to those use for pwelch described in Chapter 3, although the order is different. TLFeBOOK Time–Frequency Analysis 157 [B,f,t] = specgram(x,nfft,fs,window,noverlap) where the output, B , is a complex matrix containing the magnitude and phase of the STFT time–frequency spectrum with the rows encoding the time axis and the columns representing the frequency axis. The optional output arguments, f and t , are time and frequency vectors that can be helpful in plotting. The input arguments include the data vector, x , and the size of the Fourier transform win- dow, nfft . Three optional input arguments include the sampling frequency, fs , used to calculate the plotting vectors, the window function desired, and the number of overlapping points between the windows. The window function is specified as in pwelch : if a scalar is given, then a Hanning window of that length is used. The output of all MATLAB-based time–frequency methods is a function of two variables, time and frequency, and requires either a three-dimensional plot or a two-dimensional contour plot. Both plotting approaches are available through MATLAB standard graphics and are illustrated in the example below. Example 6.1 Construct a time series consisting of two sequential sinu- soids of 10 and 40 Hz, each active for 0.5 sec (see Figure 6.2). The sinusoids should be preceded and followed by 0.5 sec of no signal (i.e., zeros). Determine the magnitude of the STFT and plot as both a three-dimensional grid plot and as a contour plot. Do not use the Signal Processing Toolbox routine, but develop code for the STFT. Use a Hanning window to isolate data segments. Example 6.1 uses a function similar to MATLAB’s specgram , except that the window is fixed (Hanning) and all of the input arguments must be specified. This function, spectog , has arguments similar to those in specgram . The code for this routine is given below the main program. F IGURE 6.2 Waveform used in Example 6.1 consisting of two sequential sinu- soids of 10 and 40 Hz. Only a portion of the 0.5 sec endpoints are shown. TLFeBOOK 158 Chapter 6 % Example 6.1 and Figures 6.2, 6.3, and 6.4 % Example of the use of the spectrogram % Uses function spectog given below % clear all; close all; % Set up constants fs = 500; % Sample frequency in Hz N = 1024; % Signal length f1 = 10; % First frequency in Hz f2 = 40; % Second frequency in Hz nfft = 64; % Window size noverlap = 32; % Number of overlapping points (50%) % % Construct a step change in frequency tn = (1:N/4)/fs; % Time vector used to create sinusoids x = [zeros(N/4,1); sin(2*pi*f1*tn) ’; sin(2*pi*f2*tn)’ zeros(N/4,1)]; t = (1:N)/fs; % Time vector used to plot plot(t,x,’k’); labels % Could use the routine specgram from the MATLAB Signal Processing % Toolbox: [B,f,t] = specgram(x,nfft,fs,window,noverlap), % but in this example, use the “spectog” function shown below. F IGURE 6.3 Contour plot of the STFT of two sequential sinusoids. Note the broad time and frequency range produced by this time–frequency approach. The ap- pearance of energy at times and frequencies where no energy exists in the origi- nal signal is evident. TLFeBOOK Time–Frequency Analysis 159 F IGURE 6.4 Time–frequency magnitude plot of the waveform in Figure 6.3 using the three-dimensional grid technique. % [B,f,t] = spectog(x,nfft,fs,noverlap); B = abs(B); % Get spectrum magnitude figure; mesh(t,f,B); % Plot Spectrogram as 3-D mesh view(160,40); % Change 3-D plot view axis([0 2 0 100 0 20]); % Example of axis and xlabel(’Time (sec)’); % labels for 3-D plots ylabel(’Frequency (Hz)’); figure contour(t,f,B); % Plot spectrogram as contour plot labels and axis The function spectog is coded as: function [sp,f,t] = spectog(x,nfft,fs,noverlap); % Function to calculate spectrogram TLFeBOOK 160 Chapter 6 % Output arguments % sp spectrogram % t time vector for plotting % f frequency vector for plotting % Input arguments % x data % nfft window size % fs sample frequency % noverlap number of overlapping points in adjacent segments % Uses Hanning window % [N xcol] = size(x); if N < xcol x = x’; % Insure that the input is a row N = xcol; % vector (if not already) end incr = nfft—noverlap; % Calculate window increment hwin = fix(nfft/2); % Half window size f = (1:hwin)*(fs/nfft); % Calculate frequency vector % Zero pad data array to handle edge effects x_mod = [zeros(hwin,1); x; zeros(hwin,1)]; % j = 1; % Used to index time vector % Calculate spectra for each window position % Apply Hanning window for i = 1:incr:N data = x_mod(i:i؉nfft-1) .* hanning(nfft); ft = abs(fft(data)); % Magnitude data sp(:,j) = ft(1:hwin); % Limit spectrum to meaningful % points t(j) = i/fs; % Calculate time vector j = j ؉ 1; % Increment index end Figures 6.3 and 6.4 show that the STFT produces a time–frequency plot with the step change in frequency at approximately the correct time, although neither the step change nor the frequencies are very precisely defined. The lack of finite support in either time or frequency is evidenced by the appearance of energy slightly before 0.5 sec and slightly after 1.5 sec, and energies at frequen- cies other than 10 and 40 Hz. In this example, the time resolution is better than the frequency resolution. By changing the time window, the compromise be- tween time and frequency resolution could be altered. Exploration of this trade- off is given as a problem at the end of this chapter. A popular signal used to explore the behavior of time–frequency methods is a sinusoid that increases in frequency over time. This signal is called a chirp TLFeBOOK Time–Frequency Analysis 161 signal because of the sound it makes if treated as an audio signal. A sample of such a signal is shown in Figure 6.5. This signal can be generated by multiplying the argument of a sine function by a linearly increasing term, as shown in Exam- ple 6.2 below. Alternatively, the Signal Processing Toolbox contains a special function to generate a chip that provides some extra features such as logarithmic or quadratic changes in frequency. The MATLAB chirp routine is used in a latter example. The output of the STFT to a chirp signal is demonstrated in Figure 6.6. Example 6.2 Generate a linearly increasing sine wave that varies be- tween 10 and 200 Hz over a 1sec period. Analyze this chirp signal using the STFT program used in Example 6.1. Plot the resulting spectrogram as both a 3- D grid and as a contour plot. Assume a sample frequency of 500 Hz. % Example 6.2 and Figure 6.6 % Example to generate a sine wave with a linear change in frequency % Evaluate the time–frequency characteristic using the STFT % Sine wave should vary between 10 and 200 Hz over a 1.0 sec period % Assume a sample rate of 500 Hz % clear all; close all; % Constants N = 512; % Number of points F IGURE 6.5 Segment of a chirp signal, a signal that contains a single sinusoid that changes frequency over time. In this case, signal frequency increases linearly with time. TLFeBOOK [...]... Wigner-Ville and Choi-Williams Assume a sample frequency of 50 0 Hz, and use analytical signal 4 Repeat Problem 3 above using the Born-Jordan-Cohen and RihaczekMargenau distributions 5 Construct a signal consisting of two sine waves of 20 and 100 Hz Add to this signal Gaussian random noise having a variance equal to 1/4 the amplitude of the sinusoids Analyze this signal using the Wigner-Ville and Choi-Williams... appropriate time and frequency centers % % % % % % Example 7.2 and Figure 7.2 Plot of wavelet boundaries for various values of ’a’ Determines the time and scale range of the Mexican wavelet Uses the equations for center time and frequency and for time and frequency spread given in Eqs (7–10) TLFeBOOK 186 Chapter 7 clear all; close all; N = 1000; fs = 1000; wo1 = pi * sqrt(2/log2(2)); a = [ .5 1.0 2.0 3.0];... example, see Figure 6.10 and Figure 6.11), and these operations should be eliminated in more efficient code TLFeBOOK Time–Frequency Analysis 171 FIGURE 6.11 The determining function of the Rihaczek-Margenau distribution Example 6 .5 Compare the Choi-Williams and Rihaczek-Margenau distributions for both a double sinusoid and chirp stimulus Plot the RihaczekMargenau determining function* and the results using... calculated using the FFT The center time, t0, and center frequency, w0, are constructed by direct application of Eqs (8) and (10) Note that since the wavelet is constructed symmetrically about t = 0, the center time, t0, will always be zero, and an appropriate offset time, t1, is added during plotting The time and frequency boundaries are calculated using Eqs (7) and (9), and the resulting boundaries as are... frequency of 50 0 Hz and a total time of one second 2 Rerun the two examples that involve the Wigner-Ville distribution (Examples 6.3 and 6.4), but use the real signal instead of the analytic signal Plot the results as both 3-D mesh plots and contour plots 3 Construct a signal consisting of two components: a continuous sine wave of 20 Hz and a chirp signal that varies from 20 Hz to 100 Hz over a 0 .5 sec time... 2), and the coefficients are plotted three-dimensionally against the values of a and b The resulting plot, Figure 7.3, reflects the time–frequency characteristics of the signal which are quantitatively similar to those produced by the STFT and shown in Figure 6.2 % Example 7.1 and Figure 7.3 % Generate 2 sinusoids that change frequency in a step-like % manner % Apply the continuous wavelet transform and. .. RihaczekMargenau determining function* and the results using 3-D type plots % Example 6 .5 and various figures % Example of the use of Cohen’s class distributions applied to % both sequential sinusoids and a chirp signal % clear all; close all; global G; % Set up constants (Same as in previous examples) fs = 50 0; % Sample frequency N = 256 ; % Signal length f1 = 20; % First frequency in Hz f2 = 100; % Second frequency... FIGURE 7.1 A mother wavelet (a = 1) with two dilations (a = 2 and 4) and one contraction (a = 0 .5) where b acts to translate the function across x(t) just as t does in the equations above, and the variable a acts to vary the time scale of the probing function, ψ If a is greater than one, the wavelet function, ψ, is stretched along the time axis, and if it is less than one (but still positive) it contacts... those of Example 6.1 % Example 6.3 and Figures 6.7 and 6.8 % Example of the use of the Wigner-Ville distribution % Applies the Wigner-Ville to data similar to that of Example % 6.1, except that the data has been shortened from 1024 to 51 2 % to improve run time % clear all; close all; % Set up constants (same as Example 6–1) fs = 50 0; % Sample frequency N = 51 2; % Signal length f1 = 10; % First frequency... wavelet analysis, and is defined by the equation: 2 ψ(t) = e−t cos(π √ln2 2 t) (5) *Individual members of the wavelet family are specified by the subscripts a and b; i.e., ψa,b The mother wavelet, ψ1,0, should not to be confused with the mother of all Wavelets which has yet to be discovered TLFeBOOK 180 Chapter 7 The wavelet coefficients, W(a,b), describe the correlation between the waveform and the wavelet . sinu- soids of 10 and 40 Hz, each active for 0 .5 sec (see Figure 6.2). The sinusoids should be preceded and followed by 0 .5 sec of no signal (i.e., zeros). Determine the magnitude of the STFT and plot. of two sequential sinu- soids of 10 and 40 Hz. Only a portion of the 0 .5 sec endpoints are shown. TLFeBOOK 158 Chapter 6 % Example 6.1 and Figures 6.2, 6.3, and 6.4 % Example of the use of the. evidenced by the appearance of energy slightly before 0 .5 sec and slightly after 1 .5 sec, and energies at frequen- cies other than 10 and 40 Hz. In this example, the time resolution is better